Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions. Although this is a mouthful and a little hard to grasp on first read, it is much more readily understood when this is referred to by it more popular name—the butterfly effect. Briefly, the butterfly effect poses that small differences in initial conditions (such as those due to rounding errors in numerical computation) yield widely diverging outcomes for such dynamical systems, rendering long-term prediction impossible in general. The name of the effect, coined by Edward Lorenz, is derived from the theoretical example of a hurricane's formation being contingent on whether or not a distant butterfly had flapped its wings several weeks before. Although the butterfly effect may appear to be an esoteric and unlikely behavior, it is exhibited by very simple systems: for example, a ball placed at the crest of a hill may roll into any of several valleys depending on, among other things, slight differences in initial position.
In many scenarios, it can be rather important to identify and correlate causes and effects. One can appreciate that such capabilities would have great use and advantage in any of a wide variety of settings such as, production, healthcare, productivity, athletics, stock prices, etc., as a few non-limiting examples. Thus, being able to identify a nexus between causes and effects in several such scenarios, and then being able to take action based on this information, could be the difference between (a) producing products that avoid or result in a product liability law suit, (b) life or death of a patient, (c) meeting or blowing through a deadline due to productivity, (d) winning or losing the SUPER BOWL or (e) a rise or fall in a companies stock price.
However, as clearly seen by the butterfly effect example, the multitude of causes and the multitude of effects can create an infinite list of items that need to be tracked, observed and analyzed. In addition, in many situations the ability to draw a connection between the cause and effect may be difficult, impossible or at least unverifiable, which simply leads to speculation. However, rather than having absolute certainty with regards to causes and effects, there is at least a benefit that can be derived from assigning probabilities to various causes and effects.
Thus, there is a need in the art for a solution that can help track various causes and effects and to identify some level of probability with regards to the nexus of certain causes and effects. In our electronic and connected world, much automation can be implemented to provide assistance in the tracking and analysis of causes and effects.